Clinical Trial Explores New Frontier in CAR-T Cell Therapy for Lymphoma
Published
Duke Cancer Institute is participating in a national clinical trial that could reshape the future of CAR T-cell therapy for patients with aggressive lymphoma. Led by industry sponsors and involving multiple institutions, the ALPHA3 trial investigates the potential of allogeneic CAR T-cell treatments to prevent relapse in high-risk patients who appear to be in remission following initial chemotherapy.
Traditional CAR-T therapies use a patient’s own T-cells, known as autologous CAR T-cells, which are genetically modified to target and destroy cancer cells. While effective, this process is complex, time-consuming, and can cause significant side effects due to the intensity of the immune response.
The ALPHA3 trial takes a different approach. Instead of relying on a patient’s own cells, it uses donor-derived allogeneic CAR T-cells. These cells are manufactured in bulk, frozen, and ready for use, offering a faster, potentially less toxic alternative. However, because these cells are foreign to the patient’s body, they are often eliminated by the immune system within weeks, limiting their window of effectiveness.
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Results from ALPHA3 could offer new hope for patients with hard-to-treat lymphomas and redefine how remission is maintained.
ALPHA3 is also unique in its focus on patients who are in remission but still at high risk of relapse. After completing chemotherapy, participants undergo a highly sensitive test for measurable residual disease (MRD), a “needle in a haystack” test capable of detecting one cancer cell among millions. If MRD is detected, patients are randomized to receive either allogeneic CAR-T therapy or standard monitoring.
“The hope is that the allogeneic CAR-T will have enough potency to kill off the small number of cancer cells that are hiding in a deep, dark place in the body,” said Mitchell Horwitz, MD, cellular therapy specialist with DCI’s Hematologic Malignancies and Cellular Therapy disease program.
For patients, the trial offers a chance to gain deeper insight into their post-treatment status and potentially receive an intervention that could prevent relapse. From a provider’s perspective, the trial could pave the way for broader use of allogeneic CAR-T therapies.
This multisite trial is highly selective, requiring patients to meet strict criteria for disease risk and MRD positivity.
While early in its implementation, this trial represents a promising evolution in CAR-T therapy.
“These allogeneic CAR-T therapies could be cheaper, quicker to obtain, and less toxic than autologous CAR-T products,” Horwitz said.
Researchers at the Duke Cancer Institute (DCI) are working with collaborators at leading academic medical centers to explore a faster, less invasive way to detect and analyze head and neck cancers using light and artificial intelligence.In a recent study published in Biophotonics Discovery, a DCI team led by Tuan Vo-Dinh, PhD, partnered with Maie St John, MD, and clinical researchers at the University of California, Los Angeles (UCLA), to demonstrate how a light-based imaging technique combined with machine learning could help distinguish cancerous tissue with high accuracy. The work is now continuing at Johns Hopkins University.Today, diagnosing many cancers such as thyroid and head and neck cancers often relies on pathology or fine needle aspiration (FNA). In an FNA procedure, a thin needle is inserted into a tumor to collect cells, which are then analyzed by a pathologist.“While these approaches are widely used, they can be time-consuming and don’t always give clear or definitive results,” Vo-Dinh said. “In some cases, patients may have to wait weeks for answers, and the accuracy can vary depending on sampling and interpretation.”The collaborative teams used a technology known as Dynamic Optical Contrast Imaging (DOCI), a technique developed at UCLA, which uses laser light to excite tissue. When exposed to this light, different molecules in the tissue emit fluorescence—subtle signals that vary depending on whether tissue is cancerous or healthy. The result is a color-coded “map” of the tissue that reflects its biological properties.“Instead of looking at one spot on the tissue sample, this technique provides spatial information across the entire tissue region monitored,” Vo-Dinh explained. “You can actually see differences in how cancerous and non-cancerous areas respond to light.”These light-based signals are incredibly rich—but also complex. That’s where machine learning comes in. Vo-Dinh’s team applied machine learning algorithms to analyze the complex imaging data generated by DOCI. The researchers trained AI models to recognize subtle patterns in the imaging data and distinguish between different types of thyroid cancer, specifically papillary and follicular thyroid cancers, and to determine whether tissue was cancerous.The results were promising. The machine learning system showed strong agreement with pathological findings, accurately identifying cancerous regions within the tissue samples.“This is an exciting proof of principle demonstration that combining photonics with AI can work together and provide meaningful, reliable answers,” Vo-Dinh said. “The performance we’re seeing so far is very encouraging.”While the research is still in its early stages, the potential implications for patient care are significant.Because this approach directly analyzes the tissue and does not rely on waiting for lab-based assays, it could eventually support faster, point-of-care cancer assessment. Imagine a future where clinicians may be able to use similar tools directly on tissue, potentially even during surgery, to guide decisions in real time.“It could reduce the need for repeat biopsies and long waiting periods,” Vo-Dinh said. “And because the technique is non-invasive, it may also make screening easier and more accessible for patients.”The work underscores the critical importance of collaboration across institutions and disciplines, bringing together clinical expertise, advanced imaging, and data science.“This is a very synergistic partnership,” Vo-Dinh said. “Each group contributes something essential, from clinical insight to optical technology to computational analysis. That kind of complementarity is what drives innovation.”Next steps will focus on expanding and refining the technology, with the goal of enabling real-time, in situ cancer detection.“We’re excited to continue this collaboration and explore how far this approach can go,” Vo-Dinh said.
Researchers at the Duke Cancer Institute (DCI) are working with collaborators at leading academic medical centers to explore a faster, less invasive way to detect and analyze head and neck cancers using light and artificial intelligence.In a recent study published in Biophotonics Discovery, a DCI team led by Tuan Vo-Dinh, PhD, partnered with Maie St John, MD, and clinical researchers at the University of California, Los Angeles (UCLA), to demonstrate how a light-based imaging technique combined with machine learning could help distinguish cancerous tissue with high accuracy. The work is now continuing at Johns Hopkins University.Today, diagnosing many cancers such as thyroid and head and neck cancers often relies on pathology or fine needle aspiration (FNA). In an FNA procedure, a thin needle is inserted into a tumor to collect cells, which are then analyzed by a pathologist.“While these approaches are widely used, they can be time-consuming and don’t always give clear or definitive results,” Vo-Dinh said. “In some cases, patients may have to wait weeks for answers, and the accuracy can vary depending on sampling and interpretation.”The collaborative teams used a technology known as Dynamic Optical Contrast Imaging (DOCI), a technique developed at UCLA, which uses laser light to excite tissue. When exposed to this light, different molecules in the tissue emit fluorescence—subtle signals that vary depending on whether tissue is cancerous or healthy. The result is a color-coded “map” of the tissue that reflects its biological properties.“Instead of looking at one spot on the tissue sample, this technique provides spatial information across the entire tissue region monitored,” Vo-Dinh explained. “You can actually see differences in how cancerous and non-cancerous areas respond to light.”These light-based signals are incredibly rich—but also complex. That’s where machine learning comes in. Vo-Dinh’s team applied machine learning algorithms to analyze the complex imaging data generated by DOCI. The researchers trained AI models to recognize subtle patterns in the imaging data and distinguish between different types of thyroid cancer, specifically papillary and follicular thyroid cancers, and to determine whether tissue was cancerous.The results were promising. The machine learning system showed strong agreement with pathological findings, accurately identifying cancerous regions within the tissue samples.“This is an exciting proof of principle demonstration that combining photonics with AI can work together and provide meaningful, reliable answers,” Vo-Dinh said. “The performance we’re seeing so far is very encouraging.”While the research is still in its early stages, the potential implications for patient care are significant.Because this approach directly analyzes the tissue and does not rely on waiting for lab-based assays, it could eventually support faster, point-of-care cancer assessment. Imagine a future where clinicians may be able to use similar tools directly on tissue, potentially even during surgery, to guide decisions in real time.“It could reduce the need for repeat biopsies and long waiting periods,” Vo-Dinh said. “And because the technique is non-invasive, it may also make screening easier and more accessible for patients.”The work underscores the critical importance of collaboration across institutions and disciplines, bringing together clinical expertise, advanced imaging, and data science.“This is a very synergistic partnership,” Vo-Dinh said. “Each group contributes something essential, from clinical insight to optical technology to computational analysis. That kind of complementarity is what drives innovation.”Next steps will focus on expanding and refining the technology, with the goal of enabling real-time, in situ cancer detection.“We’re excited to continue this collaboration and explore how far this approach can go,” Vo-Dinh said.
